Search Results (117)

Background: Antenatal depression has been associated with systemic inflammation, autonomic imbalance, and vascular dysfunction, yet its relationship with clinically relevant cardiovascular complications during pregnancy remains insufficiently defined. Objective: To evaluate whether antenatal depression diagnosed before cardiologic assessment is associated with gestational hypertension, preeclampsia, and clinically significant Holter-confirmed arrhythmias in a tertiary-care population of pregnant women referred for cardiology assessment. Methods: We conducted a retrospective secondary matched cohort analysis nested within a prospectively approved doctoral research protocol (approval no. 76/02.10.2023; approved study interval: 2 October 2023–10 February 2025), including deliveries from October 2023 to February 2025. During this 16-month interval, 12,436 deliveries were recorded. The index point was the first cardiology specialist evaluation performed between 22 + 0 and 36 + 6 weeks’ gestation. Pregnancies with a depressive disorder diagnosed by structured psychiatric interview (SCID-5) before cardiology evaluation were classified as exposed. Depression severity was categorized as mild (n = 44), moderate (n = 62), or severe (n = 24), and psychotropic medication class at index was recorded. Each depressed case was matched 1:3 with non-depressed controls by gestational age at index, calendar year, maternal age, BMI category, smoking status, and parity; adjusted models included BMI and psychotropic medication class. Results: The final referral-enriched cohort included 130 depressed pregnancies and 390 matched controls (n = 520), all of whom underwent cardiology evaluation. Between 22 + 0 and 36 + 6 weeks’ gestation, gestational hypertension occurred in 18.5% vs. 10.0% (p = 0.010), preeclampsia in 8.5% vs. 4.9% (p = 0.12), and clinically significant Holter-confirmed arrhythmias in 15.4% vs. 6.9% (p = 0.003) in depressed versus control groups, respectively. After adjustment, depression remained independently associated with gestational hypertension (aOR 1.85, 95% CI 1.12–3.05; p = 0.016) and arrhythmia (aOR 2.05, 95% CI 1.18–3.57; p = 0.011). A numerical, exploratory severity-response gradient was observed across mild, moderate, and severe depression strata, most clearly for Holter-confirmed arrhythmias; however, the severe-depression stratum was small (n = 24). Conclusions: Antenatal depression was associated with a modest but significant increase in gestational hypertension and clinically significant Holter-confirmed arrhythmias during late pregnancy among women referred for cardiology assessment. The higher preeclampsia rate in depressed pregnancies was not statistically significant. These findings support antenatal depression as a cardiovascular risk marker in gestation rather than proof of causality. Full article

Background: Cardiovascular complications are the leading cause of death in patients with end-stage renal disease (ESRD). Hemodialysis involves rapid electrolyte shifts and sudden fluid removal, which can affect ventricular repolarization and trigger arrhythmias in patients with ESRD. To enhance patient care, it is crucial to regularly assess cardiac function using noninvasive and painless methods, such as Holter electrocardiography (ECG) and routine cardiac ultrasound. These evaluations may inform improved prevention strategies to reduce the risk of elevated cardiovascular mortality rates. Methods: In total, 40 patients with ESRD on chronic hemodialysis (HD) were approached, and only 18 were enrolled from September 2024 to July 2025. Detailed medical information was provided, and written informed consent was obtained from the patient. The median duration of Holter ECG recording was 84.65 h, and cardiac ultrasound examinations were conducted. Blood gas samples were collected hourly during the second dialysis session. Results: Surprisingly, one-third of the patients opted to withdraw their consent for this painless investigation. No significant differences were observed in the QT and QTc intervals between the dialysis and non-dialysis days (p = 0.184 and p = 0.446, respectively). However, a significant increase was observed during the first 3 h of dialysis when analyzing the intradialytic period. Conclusion: Some patients showed clinically significant changes in QT and QTc intervals during treatment, which could not be confirmed statistically. Although we did not formulate a hypothesis, it is essential to recognize that patient compliance significantly influences the cardiovascular outcomes of individuals undergoing hemodialysis. Full article

Atrial fibrillation (AF) is a highly prevalent arrhythmia associated with stroke, heart failure and excess mortality. Yet, “silent” AF episodes remain undetected, leading to underestimation of disease burden. Additionally, paroxysms occur in an “unpredictable” way, and available clinical scores only stratify long-term AF risk with moderate discrimination, lacking the ability to evaluate near-term events. Artificial intelligence (AI) applied to sinus rhythm from short or continuous electrocardiogram (ECG) recordings shows that such predictive information is hidden in “plain sight.” This complementary approach seeks to uncover latent AF substrate and forecast imminent AF episodes. Deep-learning models trained on 10-s, 12-lead ECGs can identify individuals with prevalent or long- or near-term AF with areas under the curve (AUCs) up to 0.90, outperforming established clinical scores. Image-based AI-ECG models extend these capabilities to paper or scanned ECGs. Furthermore, AI algorithms applied to 24-h Holter and multi-day patch recordings achieve AUCs ≥0.80 for detecting occult AF or predicting it within 14 days, consistently surpassing risk scores like C₂HEST and HATCH. Short-term models utilizing heart-rate variability features further demonstrate that AF can be anticipated minutes to hours before onset, with accuracies around 90% in curated datasets. However, most AI-AF studies remain retrospective, single-system and focused on diagnostic yield rather than clinical outcomes like stroke or mortality. Moreover, few pragmatic trials have evaluated AI-guided AF screening and its translation into clinical benefit. Robust prospective trials and standardized evaluation frameworks are needed before AI-guided AF prediction can be routinely integrated into clinical decision-making. Full article

Noninvasive blood glucose monitoring is crucial for detecting early dysglycemia, yet continuous glucose monitors remain invasive and costly. Electrocardiogram (ECG) and its derived heart rate variability (HRV) measure may offer a noninvasive indicator of autonomic and cardiac responses associated with acute changes in glucose. In this study, 30 adults underwent a 75 g oral glucose tolerance test with concurrent ECG Holter and interstitial glucose monitoring. From these recordings, HRV and ECG features were extracted. A deep learning classifier with HRV and ECG was then trained to detect hyperglycemia (glucose ≥ 180 mg/dL). Cross-correlation analysis confirmed a significant association between HRV and glucose (Pearson r ~0.65, p < 0.05) when aligning each participant’s data according to individual response delays. The model achieved high classification performance under rigorous temporal validation (accuracy ~89%, area under the receiver operating characteristic curve ~0.89). Saliency analyses revealed that the classifier’s decisions focus on distinct ECG waveform transitions and key HRV features linked to glucose-induced autonomic changes. Overall, acute hyperglycemia elicited discernible changes in HRV and cardiac conduction, supporting the feasibility of this physiologically grounded approach for detecting the acute hyperglycemic phase under controlled conditions. This method holds promise for real-time implementation in wearable devices, enabling early diabetes risk screening. Full article

Background/Objectives: Palpitations are among the most common reasons for cardiology referrals. Despite widespread use of ambulatory cardiac monitoring, contemporary data from the Middle East are scarce. Extended Holter monitoring provides detailed evaluation of arrhythmia burden, autonomic regulation, and symptom–rhythm correlation in routine clinical practice. Methods: We conducted a prospective observational study of consecutive patients presenting with palpitations who underwent 24–96 h ambulatory Holter monitoring at a cardiology outpatient clinic in Saudi Arabia in 2025. Demographic and clinical characteristics, comorbidities, medication use, echocardiographic parameters, heart rate variability (HRV), ventricular and supraventricular ectopy, tachyarrhythmias, and symptom diary activations were systematically evaluated. Results: Among 251 patients (mean age 41.9 ± 16.4 years; 35.5% male), Holter monitoring showed excellent recording quality (mean analyzable time 98.7 ± 9.5%). Premature ventricular contractions (PVCs) were detected in 53.4% of patients, but burden was low (median 0.0%, IQR 0–0.1%), with only 4.4% exceeding 10%. Atrial premature contractions (APCs) were common (92.0%), though usually low-burden (median burden 0.0%, IQR 0–0.1%); atrial fibrillation and supraventricular tachycardia were rare (0.8% each). Symptom diary activation occurred in 116 patients (46.2%), with 996 events; most (87.9%) correlated with sinus tachycardia, while only 8.6% correlated with PVCs and 2.6% with APCs. In the remaining 53.8% of patients, no symptom–rhythm correlation was documented during monitoring. Heart rate variability showed expected age-related changes. Conclusions: In this predominantly young cohort, Holter monitoring revealed frequent low-burden atrial and ventricular ectopy, whereas clinically significant tachyarrhythmias were uncommon. Holter monitoring up to 96 h provided a diagnostic yield in approximately 50% of patients and should be considered a first-line screening tool. Patients without diagnostic findings may require prolonged monitoring using external or implantable devices. Full article

Background: Sleep bruxism involves repetitive jaw-muscle activity, including teeth clenching, grinding, or mandibular bracing. Despite the growing interest in botulinum toxin type A (BTX-A) as a therapeutic intervention for bruxism, evidence remains limited, particularly regarding studies using portable electromyography (EMG) monitoring devices. This study evaluated the effects of BTX-A injections into the masseter muscle on the reduction of bruxism activity, as measured using the portable electromyographic Holter Bruxoff system. Methods: Adult patients with diagnosed sleep bruxism were monitored for two nights using the Bruxoff device to record masseter EMG activity, respiratory rate, and heart rate. After receiving standardized bilateral masseter BTX-A injections, participants underwent the same monitoring protocol 40 days later. Statistical analyses compared pre- and post-treatment values, and effect sizes were calculated. Results: Ten participants (60% women; mean age 47.6 ± 4.4 years) completed the study. The Bruxism Index showed a marked reduction, dropping from 12.2 ± 1.32 at baseline to 7.4 ± 1.35 after 40 days, a statistically significant change (t (9) = 10.23, p < 0.001; Cohen’s d = 3.25). Average heart rate also decreased significantly, from 64.4 ± 2.99 to 62.6 ± 2.63 (t (9) = 2.86, p = 0.018; Cohen’s d = 0.91). However, the respiratory rate measurement remains stable. Conclusions: BTX-A injections into the masseter muscles produced a marked reduction in sleep-related bruxism activity as measured by portable EMG. These findings support BTX-A as a promising and effective treatment option for sleep bruxism. Full article

Introduction: Palpitations are one of the most common cardiovascular complaints, affecting approximately 6% to 11% of the general population. Since palpitations often occur sporadically and resolve before medical evaluation, diagnosing the underlying rhythm disturbance requires documentation via an electrocardiogram (ECG) recorded during the symptomatic episode. The standard tool for this purpose has long been the 24-h Holter monitor, which has significant limitations, with diagnostic yields as low as 10% to 15%. Objective: This study aims to evaluate the feasibility and diagnostic yield of single-lead ECG recordings from smartwatches in patients presenting with palpitations. Methods: From 1 May 2023 to 1 May 2025, we conducted a prospective, real-world cohort study among consecutive adults referred to the University Hospital of Ferrara-based arrhythmia outpatient clinics for evaluation of palpitations. Eligibility required patients to be ≥21 years of age, report palpitations for which ambulatory documentation was clinically indicated, and already own a compatible smartwatch capable of single-lead ECG. Participants were trained to record a 30-s single-lead ECG at the onset of symptoms. Tracings were transmitted securely and independently reviewed by two blinded electrophysiologists. Results: Fifty-nine patients were enrolled (mean age 52 years, 64% male). Thirty-one patients (52%) transmitted at least one smartwatch-derived electrocardiographic tracing. Seventy-seven smartwatch tracings were received. Of these, 73 (95%) were interpretable; 57 (78%) showed an arrhythmia, whereas 16 (22%) demonstrated normal sinus rhythm. Four recordings (5%) were non-interpretable. From the 57 arrhythmic tracings, 44 distinct arrhythmic diagnoses were identified. Paroxysmal atrial fibrillation (AF) accounted for 16 episodes. Other diagnosed arrhythmias included atrial flutter (n = 6), paroxysmal supraventricular tachycardia (PSVT) (n = 4), premature atrial complexes (PAC) (n = 6), premature ventricular complexes (PVC) (n = 9), inappropriate sinus tachycardia (n = 12), and second-degree atrioventricular (AV) block type I (n = 4). Conclusions: Smartwatch-based ECG monitoring in symptomatic patients is feasible and provides a high diagnostic yield for a broad spectrum of arrhythmias. Unlike large-scale population screening approaches, which generate vast datasets with limited clinical benefit, a symptom-driven strategy applied to carefully selected, educated, and motivated patients proves both clinically valuable and organizationally sustainable. Indeed, the mean number of tracings transmitted per patient was low (1.3), confirming the clinical and operational sustainability of this patient-triggered, real-world approach. Full article

Background/Objectives: Pulmonary vein isolation (PVI) is the gold standard for atrial fibrillation (AF) ablation. Recently, a randomized study showed that adding voltage-guided ablation (VGA) in persistent AF cases was beneficial. The aim of the present study was to evaluate the safety, efficacy and predictors of success of redo procedures after VGA in an exclusively persistent AF cohort. Methods: Data are derived from the prospective Erfurt AF ablation registry. Starting in 2015, ablation procedures were performed using CARTO3D and VGA. Patients receiving their first redo procedure between 01/2015 and 08/2022 were included. Follow-up included 72 h Holter ECG or device interrogation, ECG, symptom-triggered event recording, and questioning at 3 and 12 months after the redo procedure. The primary endpoint was freedom of recurrence of AF or atrial tachycardia (AT) without drugs between 3 and 12 months. Results: Altogether, 683 patients received a first VGA between January 2015 and May 2022, and 77 patients had their first redo procedure occurring 20 ± 17 months after the first procedure. During the first redo procedure, reconnected PVs were found in 44%, reconnected lines in 23% and new or progressive LVZs in 57% of patients. Complications occurred in two patients (2.6%). During follow-up one patient died, and one did not participate due to aphasia. It was found that 69% were free of recurrence and 61% were free of recurrence off drugs. Patients with recurrence were older than those without recurrence off drugs (73 ± 6 versus 69 ± 9 years, p = 0.049). Conclusions: Redo procedures after VGA in persistent AF have comparatively good results; although, many patients have progressive fibrosis. Full article

Background/Objectives: Portable and wearable ECG technologies are increasingly used in adult cardiac monitoring. However, evidence supporting their feasibility and diagnostic performance in pediatric populations remains limited. This systematic review evaluates the diagnostic accuracy, usability, artifact susceptibility, and user acceptance of mobile ECG technologies in pediatric cardiology. Methods: A systematic literature search was performed in the Embase, PubMed, Scopus, and Web of Science databases. The review was conducted in accordance with the PRISMA 2020 guidelines and was registered in the PROSPERO database. Results: A total of 30 publications were included in the final analysis. Portable ECG devices demonstrated good feasibility diagnostic utility in children. Handheld systems provided high-quality tracings with strong agreement with standard 12-lead ECGs and higher adherence, as well as user satisfaction compared with conventional event recorders. However, automated rhythm classification frequently misidentified pediatric arrhythmias. Smartwatch-based ECG recordings showed high diagnostic accuracy when manually interpreted, but automated algorithms were unreliable, particularly for tachyarrhythmias and conduction abnormalities. Alternative electrode placement strategies improved smartwatch performance, and patient acceptance was consistently high. ECG patch monitoring, particularly with extended-wear devices, achieved the highest diagnostic yield, detecting arrhythmias often missed by short-duration Holter monitoring while maintaining comparable signal quality. Conclusions: Mobile ECG technologies represent a promising adjunct for pediatric rhythm surveillance, offering diagnostic performance comparable to standard modalities when interpreted by clinicians and improved usability and patient acceptance. Persistent limitations include the poor reliability of adult-oriented automated algorithms and the underrepresentation of younger children and the predominantly off-label use of these devices in pediatric populations, underscoring the need for pediatric-specific algorithm development and age-adapted device design. Full article

Background: Postoperative atrial fibrillation (POAF) after cardiac surgery is common and clinically relevant, yet optimal postdischarge ECG surveillance remains undefined. We assessed the incidence of POAF after isolated coronary artery bypass grafting (CABG) and surgical aortic valve replacement (SAVR) using a dual-modality ambulatory strategy. Methods: In an exploratory, single-center study, consecutive adults without pre-operative AF undergoing elective isolated CABG or SAVR received dual-modality monitoring after discharge: continuous patch-Holter for ~10 days and a patient-activated single-lead recorder for up to 30 days. Early POAF was AF/AFl during index hospitalization; late POAF was first AF/AFL detected postdischarge by either modality. Results: Fifty-five patients were enrolled (CABG 30 [54.5%], SAVR 25 [45.5%]; mean age 64.6 ± 9.8 years; 38.2% women). Early POAF occurred in 10/49 (20.4%); late POAF was detected in 21/55 (38.2%). By modality, late AF was identified on the 10-day Holter in 11/51 (21.6%) and on the 30-day recorder in 19/51 (37.3%). Cumulative detection reached 20.0% by day 7, 30.9% by day 10, and 38.2% thereafter, demonstrating that a substantive proportion of late POAF occurred after day 10, and 19/21 (90%) were captured by event monitoring. Female sex was independently associated with late POAF (OR 3.70, 95% CI 1.17–11.72); longer aortic cross-clamp time was related to late POAF in the SAVR subset, while larger LA size was related to POAF incidence in the CABG group. Early (in-hospital) POAF was associated with subsequent late POAF (p = 0.025). The difference in late POAF frequency between CABG and SAVR (33.3% vs. 44.0%; p = 0.42) was not significant. Conclusions: Among patients without prior AF undergoing CABG or SAVR, late POAF is frequent and often manifests beyond 10 days after discharge. Extending ambulatory surveillance to 30 days—or adopting a 10-day continuous plus patient-activated to day 30 hybrid—materially improves case finding and should be considered in routine postoperative pathways. Full article

Accurate detection of premature atrial contractions (PACs) and premature ventricular contractions (PVCs) in single-lead electrocardiograms (ECGs) is crucial for early identification of patients at risk for atrial fibrillation, cardiomyopathy, and other adverse outcomes. In this work, we present a fully convolutional one-dimensional U-Net that reframes beat classification as a segmentation task and directly detects normal beats, PACs, and PVCs from raw ECG signals. The architecture employs a ConvNeXt V2 encoder with simple decoder blocks and does not rely on explicit R-peak detection, handcrafted features, or fixed-length input windows. The model is trained on the Icentia11k database and an in-house single-lead ECG dataset that emphasizes challenging, noisy recordings, and is validated on the CPSC2020 database. Generalization is assessed across several benchmark and clinical datasets, including MIT-BIH Arrhythmia (ADB), MIT 11, AHA, NST, SVDB, CST STRIPS, and CPSC2020. The proposed method achieves near-perfect QRS detection (sensitivity and precision up to 0.999) and competitive PVC performance, with sensitivity ranging from 0.820 (AHA) to 0.986 (MIT 11) and precision up to 0.993 (MIT 11). PAC detection is more variable, with sensitivities between 0.539 and 0.797 and precisions between 0.751 and 0.910, yet the resulting F1-score of 0.72 on SVDB exceeds that of previously published approaches. Model interpretability is addressed using Layer-wise Gradient-weighted Class Activation Mapping (LayerGradCAM), which confirms physiologically plausible attention to QRS complexes for PVCs and to P-waves for PACs. Overall, the proposed framework provides a robust, interpretable, and hardware-efficient solution for joint PAC and PVC detection in noisy, single-lead ECG recordings, suitable for integration into Holter and wearable monitoring systems. Full article

Atrial fibrillation (AF) is the most common sustained arrhythmia and a growing global health burden, yet conventional monitoring with Holter devices, event recorders and implantable loop recorders often fails to adequately capture recurrence. Rapid advances in digital health, wearable biosensors and artificial intelligence (AI) have transformed consumer smartwatches and wearables into potential clinical tools capable of continuous, real-world rhythm surveillance. This narrative review synthesizes contemporary evidence on smartwatch-based AF monitoring, spanning core technologies—photoplethysmography, single-lead electrocardiography and AI fusion algorithms—and validation studies across post-ablation follow-up. Compared with traditional modalities, smartwatch-based AF monitoring demonstrates improved detection of AF recurrence, enhanced characterization of AF burden, symptom–rhythm correlation, and greater patient engagement. At the same time, key limitations are critically examined, including motion artifacts, false-positive alerts, short recording windows, adherence dependence, digital literacy and access gaps, as well as unresolved issues around regulation, interoperability and data privacy. By integrating engineering advances with guideline-directed care pathways, smartwatch-based AF monitoring holds promise to complement, rather than immediately replace, established diagnostic tools and to enable more proactive, individualized AF management. Future work must focus on robust clinical validation, equitable implementation and clear regulatory frameworks to safely scale these technologies. Full article

Heart rate variability (HRV) reflects autonomic nervous system (ANS) activity through nonlinear mechanisms, including complex beat-to-beat fluctuations and chaos-like dynamics of healthy cardiac rhythms. However, conventional HRV indices often show inconsistent interpretations of sympathetic activity across studies, making its evaluation difficult. Recently, nonlinear indices such as Acceleration Capacity (AC), Deceleration Capacity (DC), and the non-Gaussianity index have been introduced to separately evaluate sympathetic and parasympathetic nervous activity. We hypothesized that these indices are applicable in dogs and could reflect age-related changes in ANS function. This study provides the first longitudinal evaluation of nonlinear HRV indices, including AC, DC, and the non-Gaussianity index, during puppy growth, highlighting new insights into autonomic maturation in young dogs. HRV was longitudinally measured in eight healthy puppies (4 to 12 months old) of various breeds every two months. HRV analysis was conducted during sleep using 48-h Holter ECG recordings. We applied frequency-domain (nHF, LF/HF), time-domain (SDNN, RMSSD), geometric (SD1, SD2, SD1/SD2), and nonlinear analyses (AC, DC, non-Gaussianity index). Parasympathetic indicators (RMSSD, SD1) and overall HRV indices (SDNN, SD2) significantly increased with age, while the sympathetic indicator AC significantly decreased. No significant changes were observed in nHF, LF/HF, SD1/SD2, DC, or the non-Gaussianity index. HRV analysis suggests that puppies are initially sympathetic-dominant, shifting toward parasympathetic dominance as they grow. AC may serve as a useful marker of sympathetic activity in young dogs, whereas DC and the non-Gaussianity index may provide stable ANS assessments regardless of age. Full article

Background: Optimizing training load and recovery is crucial for achieving peak performance in competitive wrestling, a sport characterized by high physical, technical, and psychological demands. Methods: This study compared the effects of two different training programs—one emphasizing high-intensity interval training (HIIT) sessions and the other based on traditional volume-oriented training—on both competitive performance and autonomic regulation measured by heart rate variability (HRV). A total of 24 elite wrestlers were divided into two equal groups, each following a different weekly training regimen over a 3-month period. HRV was recorded using a wearable 3-channel ECG Holter before training, immediately after training, and during recovery phases (up to 2 h post-exercise). HRV parameters were analyzed to assess training-induced stress and recovery status. Competitive performance was evaluated using official national championship scores and ranking positions. Results: Both training programs improved competitive performance, the HIIT-based regimen induced greater short-term suppression of parasympathetic activity (RMSSD: −32% vs. −14%; HF power: −40% vs. −18%) and increased sympathetic dominance (LF/HF: +56% vs. +22%) after training. Wrestlers in the HIIT group achieved a mean competition score of 17.92 ± 4.50 points, compared to 15.08 ± 6.26 points in the volume-oriented group. These acute autonomic shifts may provide a higher readiness for intense and explosive actions, which is advantageous in short and dynamic matches. In contrast, the volume-oriented program induced smaller acute autonomic changes but showed a slower recovery to baseline. Conclusions: These findings suggest that HRV-derived measures can serve as sensitive indicators of training load tolerance, recovery capacity, and stress susceptibility in combat sports athletes. This study highlights the value of integrating HRV monitoring into the periodization of combat training to individualize the load, prevent overtraining, and optimize performance outcomes. Full article

Background: Elite athletes are frequently subjected to high-intensity training regimens, which can result in cumulative physical stress, overtraining, and potential health risks. Monitoring autonomic responses to such load is essential for optimizing performance and preventing maladaptation. Objective: The present study aimed to assess changes in autonomic regulation immediately and two hours after training in athletes, using an integrated framework (combining time- and frequency-domain HRV indices with nonlinear and recurrence quantification analysis). It was investigated how repeated assessments over a 4-month period can reveal cumulative effects and identify athletes at risk. Special attention was paid to identifying signs of excessive fatigue, autonomic imbalance, and cardiovascular stress. Methods: Holter ECGs of 12 athletes (mean age 21 ± 2.22 years; males, athletes participating in competitions) over a 4-month period were recorded before, immediately after, and two hours after high-intensity training, with HRV calculated from 5-min segments. Metrics included HRV and recurrent quantitative analysis. Statistical comparisons were made between the pre-, post-, and recovery phases to quantify autonomic changes (repeated-measures ANOVA for comparisons across the three states, paired t-tests for direct two-state contrasts, post hoc analyses with Holm–Bonferroni corrections, and effect size estimates η²). Results: Immediately after training, significant decreases in SDNN (↓ 35%), RMSSD (↓ 40%), and pNN50 (↓ 55%), accompanied by increases in LF/HF (↑ 32%), were observed. DFA α1 and Recurrence Rate increased, indicating reduced complexity and more structured patterns of RR intervals. After two hours of recovery, partial normalization was observed; however, RMSSD (−18% vs. baseline) and HF (−21% vs. baseline) remained suppressed, suggesting incomplete recovery of parasympathetic activity. Indications of overtraining and cardiac risk were found in three athletes. Conclusion: High-intensity training in elite athletes induces pronounced acute autonomic changes and incomplete short-term recovery, potentially increasing fatigue and cardiovascular workload. Longitudinal repeated testing highlights differences between well-adapted, fatigued, and at-risk athletes. These findings highlight the need for individualized recovery strategies and ongoing monitoring to optimize adaptation and minimize the risk of overtraining and health complications. Full article